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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Operationalization of Artificial Intelligence Applications in the Intensive Care Unit: A Systematic Review.

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Artificial intelligence (AI) in intensive care units (ICUs) shows limited progress from development to clinical use, with most studies stuck in early stages. A shift towards prospective testing is needed for tangible patient impact.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support Systems

Background:

  • Healthcare systems face global challenges, with artificial intelligence (AI) offering potential solutions, especially in data-rich intensive care units (ICUs).
  • AI applications in ICUs can enhance clinical decision-making, streamline workflows, and improve patient outcomes.
  • Despite AI's promise, practical implementation in clinical settings remains significantly limited.

Purpose of the Study:

  • To systematically evaluate the operationalization of AI systems within the ICU.
  • To assess the progress of AI development in ICUs over time.
  • To determine the technical maturity and identify the risk of bias in AI applications for ICUs.

Main Methods:

  • A systematic review of 5 databases (Embase, MEDLINE ALL, Web of Science, Cochrane, Google Scholar) was conducted for studies published between July 28, 2020, and June 10, 2024.
  • Studies focused on AI applications for adult ICUs using data from ICU stays.
  • Data extraction included AI aims, dataset origins, Technology Readiness Level (TRL), reporting standards, and risk of bias using PROBAST.

Main Results:

  • Out of 17,401 records, 1263 studies met inclusion criteria. 74% were TRL 4 or below, indicating early development.
  • Only 2% (25 studies) reached clinical integration (TRL≥6), with none fully implemented (TRL 9). External validation (TRL 5) was achieved by 24%.
  • 53% of studies had a high risk of bias, and only 16% referenced reporting standards, with modest adherence increase.

Conclusions:

  • AI research in intensive care has grown substantially, but clinical implementation remains stagnant.
  • A paradigm shift is needed, moving from retrospective validation to operationalization and prospective testing for clinical impact.
  • Current AI applications in ICUs require further development and rigorous validation before widespread clinical adoption.